Overview

Dataset statistics

Number of variables34
Number of observations776
Missing cells52
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory206.2 KiB
Average record size in memory272.2 B

Variable types

NUM18
CAT16

Warnings

TempIL is highly correlated with TempGLHigh correlation
TempGL is highly correlated with TempILHigh correlation
Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
SpatGL is highly correlated with SpatDistHigh correlation
SpatDist is highly correlated with SpatGLHigh correlation
Fstf has 52 (6.7%) missing values Missing
df_index has unique values Unique
UArt1 has 24 (3.1%) zeros Zeros
AUrs1 has 662 (85.3%) zeros Zeros
AUrs2 has 768 (99.0%) zeros Zeros

Reproduction

Analysis started2020-11-13 16:13:38.077048
Analysis finished2020-11-13 16:15:12.339066
Duration1 minute and 34.26 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct776
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean915.9265464
Minimum1
Maximum1866
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:12.698601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile94.75
Q1441.75
median887.5
Q31406.25
95-th percentile1787.25
Maximum1866
Range1865
Interquartile range (IQR)964.5

Descriptive statistics

Standard deviation551.3224112
Coefficient of variation (CV)0.6019286299
Kurtosis-1.258483608
Mean915.9265464
Median Absolute Deviation (MAD)480
Skewness0.08555912258
Sum710759
Variance303956.401
MonotocityStrictly increasing
2020-11-13T17:15:12.855674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
106610.1%
 
31410.1%
 
34410.1%
 
34310.1%
 
129610.1%
 
34110.1%
 
34010.1%
 
136210.1%
 
33710.1%
 
33410.1%
 
Other values (766)76698.7%
 
ValueCountFrequency (%) 
110.1%
 
710.1%
 
1210.1%
 
1610.1%
 
1710.1%
 
ValueCountFrequency (%) 
186610.1%
 
186510.1%
 
186210.1%
 
186110.1%
 
186010.1%
 

TempMax
Real number (ℝ≥0)

Distinct131
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.2396907
Minimum9
Maximum1341
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:13.015113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile21
Q160
median96
Q3147
95-th percentile354.75
Maximum1341
Range1332
Interquartile range (IQR)87

Descriptive statistics

Standard deviation126.2041225
Coefficient of variation (CV)0.9765121056
Kurtosis24.64884457
Mean129.2396907
Median Absolute Deviation (MAD)39
Skewness3.928065874
Sum100290
Variance15927.48054
MonotocityNot monotonic
2020-11-13T17:15:13.165670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
81222.8%
 
96212.7%
 
111202.6%
 
87202.6%
 
93192.4%
 
48192.4%
 
84192.4%
 
54182.3%
 
63182.3%
 
60182.3%
 
Other values (121)58275.0%
 
ValueCountFrequency (%) 
940.5%
 
1260.8%
 
1570.9%
 
18141.8%
 
21101.3%
 
ValueCountFrequency (%) 
134110.1%
 
115210.1%
 
111610.1%
 
86410.1%
 
81310.1%
 

TempAvg
Real number (ℝ≥0)

Distinct176
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.74742268
Minimum4
Maximum920
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:13.320662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12
Q133
median52
Q380.25
95-th percentile164.5
Maximum920
Range916
Interquartile range (IQR)47.25

Descriptive statistics

Standard deviation66.49257533
Coefficient of variation (CV)0.9814775633
Kurtosis42.46168195
Mean67.74742268
Median Absolute Deviation (MAD)21
Skewness4.857570493
Sum52572
Variance4421.262574
MonotocityNot monotonic
2020-11-13T17:15:13.472720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50172.2%
 
48151.9%
 
31141.8%
 
45141.8%
 
59131.7%
 
36131.7%
 
40121.5%
 
54121.5%
 
49121.5%
 
24121.5%
 
Other values (166)64282.7%
 
ValueCountFrequency (%) 
410.1%
 
560.8%
 
640.5%
 
760.8%
 
830.4%
 
ValueCountFrequency (%) 
92010.1%
 
49910.1%
 
46910.1%
 
42620.3%
 
38810.1%
 

SpatMax
Real number (ℝ≥0)

Distinct720
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8867.671392
Minimum971
Maximum44252
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:15.123065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum971
5-th percentile1785
Q13959.5
median7063.5
Q312013
95-th percentile23051.25
Maximum44252
Range43281
Interquartile range (IQR)8053.5

Descriptive statistics

Standard deviation6645.480517
Coefficient of variation (CV)0.7494053651
Kurtosis3.160937993
Mean8867.671392
Median Absolute Deviation (MAD)3666
Skewness1.553534739
Sum6881313
Variance44162411.3
MonotocityNot monotonic
2020-11-13T17:15:15.472708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
625840.5%
 
929340.5%
 
463830.4%
 
738030.4%
 
300030.4%
 
568620.3%
 
673620.3%
 
349720.3%
 
150320.3%
 
1103920.3%
 
Other values (710)74996.5%
 
ValueCountFrequency (%) 
97110.1%
 
100010.1%
 
108310.1%
 
108410.1%
 
113210.1%
 
ValueCountFrequency (%) 
4425210.1%
 
4315610.1%
 
3826210.1%
 
3549210.1%
 
3549010.1%
 

SpatAvg
Real number (ℝ≥0)

Distinct727
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4128.474227
Minimum135
Maximum16851
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:15.626453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1092.25
Q12090.25
median3562
Q35363.75
95-th percentile9773
Maximum16851
Range16716
Interquartile range (IQR)3273.5

Descriptive statistics

Standard deviation2701.481392
Coefficient of variation (CV)0.6543534593
Kurtosis2.329667432
Mean4128.474227
Median Absolute Deviation (MAD)1551.5
Skewness1.375664727
Sum3203696
Variance7298001.71
MonotocityNot monotonic
2020-11-13T17:15:15.771799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
882240.5%
 
141330.4%
 
169830.4%
 
378120.3%
 
615720.3%
 
153320.3%
 
378620.3%
 
719120.3%
 
388620.3%
 
1657120.3%
 
Other values (717)75296.9%
 
ValueCountFrequency (%) 
13510.1%
 
38710.1%
 
45810.1%
 
47610.1%
 
55910.1%
 
ValueCountFrequency (%) 
1685110.1%
 
1657120.3%
 
1552610.1%
 
1415010.1%
 
1290710.1%
 

TempDist
Real number (ℝ≥0)

Distinct25
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.958762887
Minimum0
Maximum24
Zeros2
Zeros (%)0.3%
Memory size6.1 KiB
2020-11-13T17:15:15.918765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q314
95-th percentile22
Maximum24
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.230277829
Coefficient of variation (CV)0.6256076081
Kurtosis-0.4637744481
Mean9.958762887
Median Absolute Deviation (MAD)4
Skewness0.7008416504
Sum7728
Variance38.81636182
MonotocityNot monotonic
2020-11-13T17:15:16.053668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
6719.1%
 
7648.2%
 
8547.0%
 
9536.8%
 
5526.7%
 
3486.2%
 
10486.2%
 
4435.5%
 
12364.6%
 
1334.3%
 
Other values (15)27435.3%
 
ValueCountFrequency (%) 
020.3%
 
1334.3%
 
2222.8%
 
3486.2%
 
4435.5%
 
ValueCountFrequency (%) 
24182.3%
 
23182.3%
 
22222.8%
 
21233.0%
 
20141.8%
 

SpatDist
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
773 
1519
 
1
44
 
1
18
 
1
ValueCountFrequency (%) 
077399.6%
 
151910.1%
 
4410.1%
 
1810.1%
 
2020-11-13T17:15:16.198461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.4%
2020-11-13T17:15:16.434237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:16.549172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length1
Mean length1.006443299
Min length1

Coverage
Real number (ℝ≥0)

Distinct94
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.56701031
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:16.684280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20
Q136
median51
Q367
95-th percentile91
Maximum100
Range95
Interquartile range (IQR)31

Descriptive statistics

Standard deviation21.17224341
Coefficient of variation (CV)0.4027667406
Kurtosis-0.5726651675
Mean52.56701031
Median Absolute Deviation (MAD)15
Skewness0.2420574864
Sum40792
Variance448.2638909
MonotocityNot monotonic
2020-11-13T17:15:16.830450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
44273.5%
 
42222.8%
 
59202.6%
 
36172.2%
 
53151.9%
 
60151.9%
 
37151.9%
 
39151.9%
 
55151.9%
 
45141.8%
 
Other values (84)60177.4%
 
ValueCountFrequency (%) 
510.1%
 
620.3%
 
720.3%
 
810.1%
 
910.1%
 
ValueCountFrequency (%) 
100121.5%
 
9830.4%
 
9720.3%
 
9640.5%
 
9530.4%
 

TempGL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
1
774 
3
 
2
ValueCountFrequency (%) 
177499.7%
 
320.3%
 
2020-11-13T17:15:16.978132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:17.062991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:17.145894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

SpatGL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2
773 
1
 
3
ValueCountFrequency (%) 
277399.6%
 
130.4%
 
2020-11-13T17:15:17.263664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:17.346950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:17.434864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

TempIL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
774 
2
 
2
ValueCountFrequency (%) 
-177499.7%
 
220.3%
 
2020-11-13T17:15:17.556702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:17.644350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:17.734233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.99742268
Min length1

SpatIL
Real number (ℝ)

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.652061856
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:17.842928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q13
median4
Q35
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.106028467
Coefficient of variation (CV)0.3028504199
Kurtosis-0.009885910355
Mean3.652061856
Median Absolute Deviation (MAD)1
Skewness-0.565763761
Sum2834
Variance1.223298969
MonotocityNot monotonic
2020-11-13T17:15:17.935086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
427034.8%
 
519925.6%
 
316120.7%
 
213617.5%
 
170.9%
 
-130.4%
 
ValueCountFrequency (%) 
-130.4%
 
170.9%
 
213617.5%
 
316120.7%
 
427034.8%
 
ValueCountFrequency (%) 
519925.6%
 
427034.8%
 
316120.7%
 
213617.5%
 
170.9%
 

TLCar
Real number (ℝ≥0)

Distinct529
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1510.466495
Minimum1001
Maximum1999
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:18.068296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1049.75
Q11255.75
median1525.5
Q31759.5
95-th percentile1942.5
Maximum1999
Range998
Interquartile range (IQR)503.75

Descriptive statistics

Standard deviation290.5090463
Coefficient of variation (CV)0.1923306788
Kurtosis-1.225551146
Mean1510.466495
Median Absolute Deviation (MAD)250.5
Skewness-0.05891255953
Sum1172122
Variance84395.50597
MonotocityNot monotonic
2020-11-13T17:15:18.216032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
190250.6%
 
142140.5%
 
158440.5%
 
122940.5%
 
186740.5%
 
189340.5%
 
119140.5%
 
156840.5%
 
199940.5%
 
169830.4%
 
Other values (519)73694.8%
 
ValueCountFrequency (%) 
100120.3%
 
100210.1%
 
100310.1%
 
100620.3%
 
101010.1%
 
ValueCountFrequency (%) 
199940.5%
 
199810.1%
 
199720.3%
 
199610.1%
 
199420.3%
 

TLHGV
Real number (ℝ≥0)

Distinct392
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.9523196
Minimum500
Maximum999
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:18.356072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile524
Q1620
median742.5
Q3871
95-th percentile974
Maximum999
Range499
Interquartile range (IQR)251

Descriptive statistics

Standard deviation145.0603044
Coefficient of variation (CV)0.194202897
Kurtosis-1.20803133
Mean746.9523196
Median Absolute Deviation (MAD)126.5
Skewness0.04566946891
Sum579635
Variance21042.49192
MonotocityNot monotonic
2020-11-13T17:15:18.505734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
68060.8%
 
87160.8%
 
80760.8%
 
98260.8%
 
66450.6%
 
86950.6%
 
58850.6%
 
82250.6%
 
86250.6%
 
67250.6%
 
Other values (382)72293.0%
 
ValueCountFrequency (%) 
50030.4%
 
50120.3%
 
50410.1%
 
50520.3%
 
50650.6%
 
ValueCountFrequency (%) 
99920.3%
 
99810.1%
 
99620.3%
 
99520.3%
 
99420.3%
 

Strasse
Categorical

Distinct15
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
A3
198 
A9
189 
A96
88 
A7
79 
A73
60 
Other values (10)
162 
ValueCountFrequency (%) 
A319825.5%
 
A918924.4%
 
A968811.3%
 
A77910.2%
 
A73607.7%
 
A6587.5%
 
A99303.9%
 
A92263.4%
 
A70212.7%
 
A94162.1%
 
Other values (5)111.4%
 
2020-11-13T17:15:18.652192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.3%
2020-11-13T17:15:18.778463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.326030928
Min length2

Kat
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
3
422 
7
181 
2
144 
1
 
29
ValueCountFrequency (%) 
342254.4%
 
718123.3%
 
214418.6%
 
1293.7%
 
2020-11-13T17:15:18.917422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:19.016286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:27.223337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Real number (ℝ≥0)

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.818298969
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:27.466596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.198351334
Coefficient of variation (CV)0.4562505041
Kurtosis-0.7128479285
Mean4.818298969
Median Absolute Deviation (MAD)0
Skewness-1.056777988
Sum3739
Variance4.832748587
MonotocityNot monotonic
2020-11-13T17:15:27.564574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
649563.8%
 
118123.3%
 
7709.0%
 
3243.1%
 
440.5%
 
520.3%
 
ValueCountFrequency (%) 
118123.3%
 
3243.1%
 
440.5%
 
520.3%
 
649563.8%
 
ValueCountFrequency (%) 
7709.0%
 
649563.8%
 
520.3%
 
440.5%
 
3243.1%
 

Betei
Real number (ℝ≥0)

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.340206186
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:27.669625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.178399991
Coefficient of variation (CV)0.5035453705
Kurtosis41.83137283
Mean2.340206186
Median Absolute Deviation (MAD)0
Skewness4.026433022
Sum1816
Variance1.388626538
MonotocityNot monotonic
2020-11-13T17:15:27.768075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
239651.0%
 
316421.1%
 
113016.8%
 
4597.6%
 
5162.1%
 
740.5%
 
640.5%
 
820.3%
 
1810.1%
 
ValueCountFrequency (%) 
113016.8%
 
239651.0%
 
316421.1%
 
4597.6%
 
5162.1%
 
ValueCountFrequency (%) 
1810.1%
 
820.3%
 
740.5%
 
640.5%
 
5162.1%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.869845361
Minimum0
Maximum9
Zeros24
Zeros (%)3.1%
Memory size6.1 KiB
2020-11-13T17:15:27.882679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.810086529
Coefficient of variation (CV)0.7261495659
Kurtosis-0.9130273077
Mean3.869845361
Median Absolute Deviation (MAD)1
Skewness0.8533956468
Sum3003
Variance7.896586299
MonotocityNot monotonic
2020-11-13T17:15:28.009490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
234444.3%
 
314418.6%
 
810713.8%
 
98010.3%
 
1314.0%
 
0243.1%
 
7233.0%
 
5151.9%
 
640.5%
 
440.5%
 
ValueCountFrequency (%) 
0243.1%
 
1314.0%
 
234444.3%
 
314418.6%
 
440.5%
 
ValueCountFrequency (%) 
98010.3%
 
810713.8%
 
7233.0%
 
640.5%
 
5151.9%
 

UArt2
Real number (ℝ)

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7796391753
Minimum-1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:28.123166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.620607381
Coefficient of variation (CV)4.643952607
Kurtosis0.8429057238
Mean0.7796391753
Median Absolute Deviation (MAD)0
Skewness1.648366864
Sum605
Variance13.10879781
MonotocityNot monotonic
2020-11-13T17:15:28.227483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
-161379.0%
 
97910.2%
 
8506.4%
 
3202.6%
 
260.8%
 
740.5%
 
130.4%
 
410.1%
 
ValueCountFrequency (%) 
-161379.0%
 
130.4%
 
260.8%
 
3202.6%
 
410.1%
 
ValueCountFrequency (%) 
97910.2%
 
8506.4%
 
740.5%
 
410.1%
 
3202.6%
 

AUrs1
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.19458763
Minimum0
Maximum89
Zeros662
Zeros (%)85.3%
Memory size6.1 KiB
2020-11-13T17:15:28.335572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.09185373
Coefficient of variation (CV)2.420085011
Kurtosis2.20043025
Mean11.19458763
Median Absolute Deviation (MAD)0
Skewness2.03127464
Sum8687
Variance733.9685384
MonotocityNot monotonic
2020-11-13T17:15:28.439895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
066285.3%
 
73638.1%
 
72202.6%
 
89111.4%
 
8250.6%
 
8840.5%
 
8630.4%
 
8130.4%
 
8710.1%
 
8410.1%
 
Other values (3)30.4%
 
ValueCountFrequency (%) 
066285.3%
 
72202.6%
 
73638.1%
 
7510.1%
 
7710.1%
 
ValueCountFrequency (%) 
89111.4%
 
8840.5%
 
8710.1%
 
8630.4%
 
8410.1%
 

AUrs2
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8118556701
Minimum0
Maximum89
Zeros768
Zeros (%)99.0%
Memory size6.1 KiB
2020-11-13T17:15:28.547634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.978562481
Coefficient of variation (CV)9.827562675
Kurtosis94.52119747
Mean0.8118556701
Median Absolute Deviation (MAD)0
Skewness9.786766497
Sum630
Variance63.65745926
MonotocityNot monotonic
2020-11-13T17:15:28.647086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
076899.0%
 
7520.3%
 
7320.3%
 
8910.1%
 
8410.1%
 
8110.1%
 
8010.1%
 
ValueCountFrequency (%) 
076899.0%
 
7320.3%
 
7520.3%
 
8010.1%
 
8110.1%
 
ValueCountFrequency (%) 
8910.1%
 
8410.1%
 
8110.1%
 
8010.1%
 
7520.3%
 

AufHi
Real number (ℝ)

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5476804124
Minimum-1
Maximum9
Zeros2
Zeros (%)0.3%
Memory size6.1 KiB
2020-11-13T17:15:28.757674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q33
95-th percentile4
Maximum9
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.096459635
Coefficient of variation (CV)3.827888651
Kurtosis-0.5856516027
Mean0.5476804124
Median Absolute Deviation (MAD)0
Skewness0.8169640614
Sum425
Variance4.395143
MonotocityNot monotonic
2020-11-13T17:15:28.860217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
-149163.3%
 
324131.1%
 
4243.1%
 
5121.5%
 
920.3%
 
820.3%
 
020.3%
 
210.1%
 
110.1%
 
ValueCountFrequency (%) 
-149163.3%
 
020.3%
 
110.1%
 
210.1%
 
324131.1%
 
ValueCountFrequency (%) 
920.3%
 
820.3%
 
5121.5%
 
4243.1%
 
324131.1%
 

Alkoh
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
765 
1
 
11
ValueCountFrequency (%) 
-176598.6%
 
1111.4%
 
2020-11-13T17:15:28.974994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:29.060695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:32.091516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.985824742
Min length1

Char1
Real number (ℝ)

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2796391753
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-11-13T17:15:32.199782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile5
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.935848001
Coefficient of variation (CV)-6.922663819
Kurtosis3.941778148
Mean-0.2796391753
Median Absolute Deviation (MAD)0
Skewness2.393163963
Sum-217
Variance3.747507483
MonotocityNot monotonic
2020-11-13T17:15:32.305973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
-167987.5%
 
5384.9%
 
4334.3%
 
6222.8%
 
240.5%
 
ValueCountFrequency (%) 
-167987.5%
 
240.5%
 
4334.3%
 
5384.9%
 
6222.8%
 
ValueCountFrequency (%) 
6222.8%
 
5384.9%
 
4334.3%
 
240.5%
 
-167987.5%
 

Char2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
747 
6
 
29
ValueCountFrequency (%) 
-174796.3%
 
6293.7%
 
2020-11-13T17:15:32.438763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:32.520394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:35.414254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.962628866
Min length1

Lich1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
581 
2
143 
1
 
51
-1
 
1
ValueCountFrequency (%) 
058174.9%
 
214318.4%
 
1516.6%
 
-110.1%
 
2020-11-13T17:15:35.539581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-11-13T17:15:35.626879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:38.482546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.00128866
Min length1

Lich2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
582 
4
187 
3
 
7
ValueCountFrequency (%) 
-158275.0%
 
418724.1%
 
370.9%
 
2020-11-13T17:15:38.612683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:38.696266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:41.595366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.75
Min length1

Zust1
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
548 
1
208 
2
 
18
-1
 
2
ValueCountFrequency (%) 
054870.6%
 
120826.8%
 
2182.3%
 
-120.3%
 
2020-11-13T17:15:41.719167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:41.808086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:47.592764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.00257732
Min length1

Zust2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
769 
2
 
7
ValueCountFrequency (%) 
-176999.1%
 
270.9%
 
2020-11-13T17:15:47.717035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:47.799622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:50.506124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.990979381
Min length1

Fstf
Categorical

MISSING

Distinct7
Distinct (%)1.0%
Missing52
Missing (%)6.7%
Memory size6.1 KiB
2
316 
1
265 
3
119 
S
 
11
4
 
10
Other values (2)
 
3
ValueCountFrequency (%) 
231640.7%
 
126534.1%
 
311915.3%
 
S111.4%
 
4101.3%
 
520.3%
 
F10.1%
 
(Missing)526.7%
 
2020-11-13T17:15:50.630843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-11-13T17:15:50.724992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:56.062107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.134020619
Min length1

WoTag
Categorical

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Fr
130 
Mo
117 
Di
111 
Do
106 
Sa
104 
Other values (3)
208 
ValueCountFrequency (%) 
Fr13016.8%
 
Mo11715.1%
 
Di11114.3%
 
Do10613.7%
 
Sa10413.4%
 
Mi10313.3%
 
So9812.6%
 
70.9%
 
2020-11-13T17:15:56.204104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:15:56.294775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:16:01.612071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.981958763
Min length0

FeiTag
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
752 
1
 
24
ValueCountFrequency (%) 
-175296.9%
 
1243.1%
 
2020-11-13T17:16:01.743728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:16:01.825290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:16:04.495731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.969072165
Min length1

Month
Categorical

Distinct12
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Aug
92 
Jul
79 
Apr
71 
Jun
70 
May
67 
Other values (7)
397 
ValueCountFrequency (%) 
Aug9211.9%
 
Jul7910.2%
 
Apr719.1%
 
Jun709.0%
 
May678.6%
 
Oct668.5%
 
Dec658.4%
 
Mar648.2%
 
Sep587.5%
 
Nov577.3%
 
Other values (2)8711.2%
 
2020-11-13T17:16:04.620985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:16:04.742900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-11-13T17:13:45.147479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:46.561953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:47.848412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:49.086503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:50.301211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:51.512353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:52.725345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:53.943817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:55.171040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:56.390639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:57.662508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:13:58.898798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:00.192435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:01.436769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:02.676101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:03.928944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:05.213804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:06.438997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:08.488944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:08.508308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:08.648191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:08.771685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:08.904674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:09.057441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:09.194699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:09.334954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:09.881981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:10.010813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:10.148710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:10.421840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:11.235104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:11.363501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:11.487734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:11.613355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:11.738138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:11.866681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:13.236988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:13.255379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:13.372251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:13.481497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:13.595561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:13.716800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:13.835810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:13.944190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:14.071493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:14.189751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:14.302541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:14.415788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:14.530490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-13T17:14:55.136955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:55.262818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:55.376870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:55.485096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:55.602574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:55.723248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:55.831790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:56.095160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:56.213012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:56.327979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:56.449013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:56.563069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:56.671780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:56.779817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:56.895461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:57.007708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:57.118793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:58.661520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:58.682250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:58.813778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:58.931814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:59.049326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:59.168841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:59.281430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:59.396912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:59.511458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:59.623565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:59.735181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:59.855668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:14:59.973560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:00.089904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:00.203635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:00.309607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:00.424261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:00.530797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:01.884658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:01.906456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:02.029514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:02.149774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:02.267246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:02.394868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:02.645480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:02.760234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:02.879234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:02.992782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:03.103921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:03.218557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:03.335461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:03.446196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:03.552802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:03.660583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:03.769214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:03.884075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:05.220018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:05.238376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:05.377059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:05.502638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:05.627988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:05.763843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:05.892541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:06.017296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:06.154033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:06.280194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:06.413098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:06.549813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:06.685866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:06.813680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:06.935240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:07.060034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:07.188327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:07.312899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-13T17:16:06.049737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-13T17:16:07.280198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-13T17:16:08.520395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-13T17:16:09.816641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-13T17:16:09.882742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-13T17:15:09.266236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:10.905710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:15:12.296558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
016928600034751004112-151691686A63632-1890-1-1-1-10-10-12Di1Jan
17216961299156151104312-121647670A33733-100-1-1-1-1241-12Do-1Jan
2123315419314931203412-141079686A97119-17203-1-1-10-12-13Sa-1Jan
31613245963126322202512-121098683A97119-172733-1-1-10-1123Sa-1Jan
41718959181603654602012-131581554A963623-1720-1-1-1-10-12-12Sa-1Jan
5211085260772809104412-141942930A63622-100-1-1-1-1240-11Mo-1Jan
6221026859994222905912-151610680A737642-100-1-1-1-1241-12Mo-1Jan
7231026859994222905912-151610680A737632-100-1-1-1-1241-12Mo-1Jan
824392715048762305712-151362731A72118-17304-14-1141-12Di-1Jan
928181930002742308712-141639822A67623-100-1-1-1-1242-11Mi-1Jan

Last rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
76618511178862584606607412-151823744A33642-100-1-1-1-1240-12Do1Dec
76718532424100010901008812-141475636A93139-1003-1560-11-13Do1Dec
76818558149308222941607112-131562890A37623200-1-1-1-1240-11Fr-1Dec
769185696641628210596306512-131688567A62622-100-1-1-1-10-10-12Fr-1Dec
7701857816126982360808612-141305511A73119-1003-16-1240-12Fr-1Dec
7711860201931499943721602912-121875579A73622-100-1-1-1-10-10-11Sa-1Dec
77218614526395527682207512-141262925A93622-100-1-1-1-10-10-11So-1Dec
7731862393113456100071107412-131850582A93734-100-1-1-1-1240-13So-1Dec
7741865937234182571407512-151950993A33632-100-1-1-1-1230-12-1Dec
7751866696214061260607812-141155500A712622-100-1-1-1-10-10-11Di-1Dec